论文标题
智能车辆的自我意识：基于经验的异常检测
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection
论文作者
论文摘要
近期智能运输系统的发展需要自动驾驶代理的发展：自我意识意识。本文旨在引入一种基于车辆内部互相关参数来检测异常的新方法。在实施机器学习之前，通过检查每个变量并创建很难跟踪的巨大嵌套条件来手动编程异常的检测。如今，可以训练动态的贝叶斯网络（DBN）模型，以自动评估和检测车辆何时可能不适。在本文中，已经设置了不同的方案，以便使用DBN模型的语义分割训练和测试开关DBN进行周边监测任务，而Hellinger距离度量指标则用于异常测量。
The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormalities were manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. Nowadays, it is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving. In this paper, different scenarios have been set in order to train and test a switching DBN for Perimeter Monitoring Task using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements.
